class_docstr: Alternating Least Squares
  
  A Recommendation Model based off the algorithms described in the paper 'Collaborative
  Filtering for Implicit Feedback Datasets' with performance optimizations described in
  'Applications of the Conjugate Gradient Method for Implicit Feedback Collaborative
  Filtering.'
  
  Parameters
  ----------
  factors : int, optional
      The number of latent factors to compute
  regularization : float, optional
      The regularization factor to use
  alpha : float, optional
      The weight to give to positive examples.
  dtype : data-type, optional
      Specifies whether to generate 64 bit or 32 bit floating point factors
  use_native : bool, optional
      Use native extensions to speed up model fitting
  use_cg : bool, optional
      Use a faster Conjugate Gradient solver to calculate factors
  iterations : int, optional
      The number of ALS iterations to use when fitting data
  calculate_training_loss : bool, optional
      Whether to log out the training loss at each iteration
  num_threads : int, optional
      The number of threads to use for fitting the model and batch recommend calls.
      Specifying 0 means to default to the number of cores on the machine.
  random_state : int, numpy.random.RandomState, np.random.Generator or None, optional
      The random state for seeding the initial item and user factors.
      Default is None.
  
  Attributes
  ----------
  item_factors : ndarray
      Array of latent factors for each item in the training set
  user_factors : ndarray
      Array of latent factors for each user in the training set
docstr: Recalculates factors for a batch of users
  
  This method recalculates factors for a batch of users and returns
  the factors without storing on the object. For updating the model
  using 'partial_fit_users'
  
  Parameters
  ----------
  userid : Union[array_like, int]
      The userid or array of userids to recalculate
  user_items : csr_matrix
      Sparse matrix of (users, items) that contain the users that liked
      each item.
docstr_tok: ["Recalculates","factors","for","a","batch","of","users","This","method","recalculates","factors","for","a","batch","of","users","and","returns","the","factors","without","storing","on","the","object",".","For","updating","the","model","using","'partial_fit_users'","Parameters","--","--","--","--","--","userid",":","Union","[","array_like",",","int","]","The","userid","or","array","of","userids","to","recalculate","user_items",":","csr_matrix","Sparse","matrix","of","(","users",",","items",")","that","contain","the","users","that","liked","each","item","."]
code: def recalculate_user(self, userid, user_items):
      """Recalculates factors for a batch of users
  
      This method recalculates factors for a batch of users and returns
      the factors without storing on the object. For updating the model
      using 'partial_fit_users'
  
      Parameters
      ----------
      userid : Union[array_like, int]
          The userid or array of userids to recalculate
      user_items : csr_matrix
          Sparse matrix of (users, items) that contain the users that liked
          each item.
      """
      user_items = check_csr(user_items)
  
      # we're using the cholesky solver here on purpose, since for a full recompute
      users = 1 if np.isscalar(userid) else len(userid)
  
      if user_items.shape[0] != users:
          raise ValueError(
              "user_items should have one row for every item in user"
          )
  
      if self.alpha != 1.0:
          user_items = self.alpha * user_items
  
      user_factors = np.zeros((users, self.factors), dtype=self.dtype)
      _als._least_squares(
          self.YtY,
          user_items.indptr,
          user_items.indices,
          user_items.data.astype("float32"),
          user_factors,
          self.item_factors,
          self.regularization,
          num_threads=self.num_threads,
      )
      return user_factors[0] if np.isscalar(userid) else user_factors
  
imports: import functools
  import heapq
  import logging
  import time
  import numpy
  import scipy
  import scipy.sparse
  from tqdm.auto import tqdm
  from ..utils import check_blas_config, check_csr, check_random_state, nonzeros
  from .None import _als
  from .matrix_factorization_base import MatrixFactorizationBase
label_desc: Use image node_id 3 for calling the AlternatingLeastSquares obj's underlying member method code with example usage: obj.recalculate_user(userid, user_items) and returns: unknown